About this Research Topic
Monitoring reliably and consistently the health of babies during childbirth remains a massive unmet clinical need worldwide. Hypoxic-ischaemic encephalopathy (HIE) continues to be an important cause of stillbirth, neonatal encephalopathy, and neonatal mortality, both in high and low-resource countries. Intrapartum-related events are estimated to account for nearly a quarter of neonatal deaths and are second only to prematurity as a cause of neonatal mortality. HIE is estimated to account for 920,000 neonatal deaths every year and is associated with another 1.1 million intrapartum stillbirths. Moreover, those who survive (more than a million per year) develop different important sequelae, from cerebral palsy to mental retardation, learning difficulties and other disabilities. In the United States, Europe and in most technologically advanced countries, the incidence of HIE is 1-4 cases per 1000 births.
Monitoring of fetal well-being during labor has for many years been performed with intermittent fetal heart rate (FHR) auscultation or continuous cardiotocography (CTG). The complex information hidden within the fetal heartbeat fluctuations is, to date, mostly displayed on a paper strip over many hours, and it is still largely examined visually, remaining a significant source of controversy and uncertainty. To address the need for better monitoring of fetal health during labor, research is necessary that is highly multidisciplinary in nature, including experts from clinical, preclinical, bioengineering, physics and mathematical domains, often working together.
With the arrival of electronic patient records, advanced computational methods, the widespread use of machine learning tools, and, more recently, of deep learning strategies, we are just beginning to harness the power of the complex information contained in routinely collected maternal-fetal data. With this goal, we recently conducted the third Signal Processing and Monitoring in Labour
workshop. It gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on:
• New methods for noninvasive CTG and electrocardiogram acquisition and analyses, including technologies for low resource settings;
• The clinical perspective of obstetricians and neonatologists, and a round table discussion by clinical experts examining several case studies from clinical practice;
• Automated CTG assessment relying on statistical analysis, machine learning, big-data processing;
• Insights from computerized analysis applied to data from animal experimental models mimicking labor;
• The role of clinical risk factors for CTG interpretation.
There is a clear need for close multidisciplinary collaboration, for the creation of new, curated CTG/FHR datasets, and particularly close collaboration between computing/data science and clinical experts. We believe that substantial progress will be possible with such multidisciplinary collaborative research.
Therefore, this Research Topic is looking for high impact research (Perspectives, Reviews, Case Studies, Research Articles) from contributors around the world that can help us create an updated view of the current challenges, recent achievements, and next steps to advance in our quest to create new bedside technologies which help clinicians to deliver healthy babies. As highly relevant, we also welcome insights from maternal-fetal data in the antepartum period as well as neurological development in children from birth on and long past the first years of life.
We hope that this Research Topic, with contributions from the Signal Processing and Monitoring in Labour workshop and beyond, will foster the investigation of new modern and advanced tools dedicated to data information processing, including analysis, diagnosis, data visualization to permit interdisciplinary research to actually exist, education and training. We do believe that the related challenges, well exposed and addressed in this Research Topic, will motivate new generations of young scientists from different fields (medicine, mathematics, statistics, engineering, bioengineering, physics) to join their expertise and work towards improved clinical care at the bedside.
Topic Editor Dr Martin Frasch has an aECG patent (WO2018160890A1) and a fetal EEG patent (US9215999B2). He has two start-ups Fetal Precision LLC and Health Stream Analytics LLC to develop fetal ECG and EEG technologies. Other Topic Editors have no conflicts of interest to declare.
Monitoring of fetal well-being during labor has for many years been performed with intermittent fetal heart rate (FHR) auscultation or continuous cardiotocography (CTG). The complex information hidden within the fetal heartbeat fluctuations is, to date, mostly displayed on a paper strip over many hours, and it is still largely examined visually, remaining a significant source of controversy and uncertainty. To address the need for better monitoring of fetal health during labor, research is necessary that is highly multidisciplinary in nature, including experts from clinical, preclinical, bioengineering, physics and mathematical domains, often working together.
With the arrival of electronic patient records, advanced computational methods, the widespread use of machine learning tools, and, more recently, of deep learning strategies, we are just beginning to harness the power of the complex information contained in routinely collected maternal-fetal data. With this goal, we recently conducted the third Signal Processing and Monitoring in Labour
workshop. It gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on:
• New methods for noninvasive CTG and electrocardiogram acquisition and analyses, including technologies for low resource settings;
• The clinical perspective of obstetricians and neonatologists, and a round table discussion by clinical experts examining several case studies from clinical practice;
• Automated CTG assessment relying on statistical analysis, machine learning, big-data processing;
• Insights from computerized analysis applied to data from animal experimental models mimicking labor;
• The role of clinical risk factors for CTG interpretation.
There is a clear need for close multidisciplinary collaboration, for the creation of new, curated CTG/FHR datasets, and particularly close collaboration between computing/data science and clinical experts. We believe that substantial progress will be possible with such multidisciplinary collaborative research.
Therefore, this Research Topic is looking for high impact research (Perspectives, Reviews, Case Studies, Research Articles) from contributors around the world that can help us create an updated view of the current challenges, recent achievements, and next steps to advance in our quest to create new bedside technologies which help clinicians to deliver healthy babies. As highly relevant, we also welcome insights from maternal-fetal data in the antepartum period as well as neurological development in children from birth on and long past the first years of life.
We hope that this Research Topic, with contributions from the Signal Processing and Monitoring in Labour workshop and beyond, will foster the investigation of new modern and advanced tools dedicated to data information processing, including analysis, diagnosis, data visualization to permit interdisciplinary research to actually exist, education and training. We do believe that the related challenges, well exposed and addressed in this Research Topic, will motivate new generations of young scientists from different fields (medicine, mathematics, statistics, engineering, bioengineering, physics) to join their expertise and work towards improved clinical care at the bedside.
Topic Editor Dr Martin Frasch has an aECG patent (WO2018160890A1) and a fetal EEG patent (US9215999B2). He has two start-ups Fetal Precision LLC and Health Stream Analytics LLC to develop fetal ECG and EEG technologies. Other Topic Editors have no conflicts of interest to declare.
Keywords: fetal monitoring, artificial intelligence, computer-aided decision support, hypoxic-ischaemic encephelopathy
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